derivative-free-optimization

Included with Lifetime

$97 forever

Optimization without gradient information

General

What this skill does


# Derivative-Free Optimization

## Purpose

Provides optimization capabilities for problems where gradient information is unavailable or unreliable.

## Capabilities

- Nelder-Mead simplex method
- Powell's method
- Surrogate-based optimization
- Bayesian optimization
- Pattern search methods
- Trust region methods

## Usage Guidelines

1. **Method Selection**: Choose based on problem characteristics
2. **Function Evaluations**: Minimize expensive function calls
3. **Surrogate Models**: Build and refine surrogate approximations
4. **Exploration-Exploitation**: Balance search strategies

## Tools/Libraries

- scipy.optimize
- Optuna
- GPyOpt

Files: 1

Size: 0.9 KB

Complexity: 8/100

Category: General

Source: https://github.com/a5c-ai/babysitter/tree/main/library/specializations/domains/science/mathematics/skills/derivative-free-optimization

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